Constrained generation for accelerated material discovery and design using generative artificial intelligence models

Abstract

Disclosed embodiments provide methods, systems, and computer program products for implementing constrained generation for material discovery and material design using generative artificial intelligence (AI) foundation models. A disclosed non-limiting method includes providing, using one or more processors, a chemical structure at a user design interface; receiving, at a foundation model, a user selection of a portion of the chemical structure and a user-input prompt for replacing the portion, where the user-input prompt indicates a desired property of a replacement portion; and generating, by the foundation model, the replacement portion, where the replacement portion replaces the selected portion of the chemical structure and includes a generated chemical structure predicted to have the desired property.

What is claimed is: 
     
         1 . A method comprising:
 providing, using one or more processors, a chemical structure at a design interface;   receiving, at a foundation model, a user selection of a portion of the chemical structure and a user-input prompt for replacing the portion, wherein the user-input prompt indicates a desired property of a replacement portion; and   generating, by the foundation model, the replacement portion, wherein the replacement portion replaces the selected portion of the chemical structure and includes a generated chemical structure predicted to have the desired property.   
     
     
         2 . The method of  claim 1 , further comprising:
 receiving, at the foundation model, user-input interactions and user-input constraints for modifying the replacement portion; and   generating, by the foundation model, a modified chemical structure for the replacement portion based on one or more of the user-input interactions, and the user-input constraints.   
     
     
         3 . The method of  claim 1 , wherein the user-input prompt comprises at least one of natural language prompts, boundary conditions, a combination of natural language and explicit property settings. 
     
     
         4 . The method of  claim 1 , wherein the user-input prompt comprises user-input property settings that indicate the desired property of the replacement portion, wherein the user-input property settings comprise one or more of a physical property, a chemical property, a thermal property, and a mechanical property. 
     
     
         5 . The method of  claim 1 , further comprising:
 obtaining a user-input dataset of materials or compounds with a set of properties; and   receiving, at a foundation model, a user selection of a region of the dataset and one or more user-input constraints for replacing the region, wherein the one or more user-input constraints indicate a desired property of a replacement region.   
     
     
         6 . The method of  claim 5 , further comprising:
 receiving user-input structural constraints; and   generating, by the foundation model, the replacement region that replaces the selected region of one or more materials or compounds that match one or more of the user selection, the one or more user-input constraints, and the user-input structural constraints.   
     
     
         7 . The method of  claim 1 , further comprising:
 tracking and encoding at least one of user selections, user-input interactions, and user-input constraints for ingestion into the foundation model for constrained generation of a modified chemical structure.   
     
     
         8 . The method of  claim 1 , further comprising:
 generating visualizations of chemical, material, or property latent space of the generated chemical structure based on one or more of user defined constraints, selections, and interaction data.   
     
     
         9 . The method of  claim 1 , further comprising:
 receiving, at the foundation model, one or more of user-input interaction data, user selection data, and user-input constraint data to generate a prompt based on in-context learning of the one or more of user-input interaction data, user selection data, and user-input constraint data.   
     
     
         10 . The method of  claim 1 , further comprising:
 receiving, at the foundation model, one or more of user-input interaction data, user selection data, and user-input constraint data; and   performing fine-tuning of the foundation model based on the one or more of user-interaction data, user selection data, and user-input constraint data.   
     
     
         11 . The method of  claim 1 , further comprising:
 performing, by the foundation model, one or more of computational chemistry processing and simulation processing based on one or more of user-input selections, and user-input constraints to generate a modified chemical structure.   
     
     
         12 . A method comprising:
 providing, using one or more processors, a chemical structure at a design interface;   receiving, at a foundation model, a user selection of a portion of the chemical structure and a user-input prompt for replacing the portion, wherein the user-input prompt indicates a desired property of a replacement portion;   generating, by the foundation model, the replacement portion, wherein the replacement portion replaces the selected portion of the chemical structure and includes a generated chemical structure predicted to have the desired property receiving user feedback based on the generated chemical structure; and   tuning the foundation model based on one or more of the user selection, the user-input prompt, the generated chemical structure, and the user feedback.   
     
     
         13 . A method comprising:
 providing, using one or more processors, a chemical structure at a design interface;   receiving, at a foundation model, a user selection of a portion of the chemical structure and a user-input prompt for replacing the portion, wherein the user-input prompt indicates a desired property of a replacement portion;   generating, by the foundation model, the replacement portion, wherein the replacement portion replaces the selected portion of the chemical structure and includes a generated chemical structure predicted to have the desired property; and
 wherein receiving, at the foundation model, further comprises receiving, by retrieval augmented generation (RAG) of the foundation model, historical data of one or more of prior user modifications or user-input constraints to one or more pre-generated datasets of chemical compounds, spectra, or similar data, and 
 sharing the historical data in response to a natural language user selection or user interaction. 
   
     
     
         14 . A system, comprising:
 one or more computer processors; and   a memory containing a program which when executed by the one or more computer processors performs an operation, the operation comprising:
 providing, using one or more processors, a chemical structure at a design interface; 
 receiving, at a foundation model, a user selection of a portion of the chemical structure and a user-input prompt for replacing the portion, wherein the user-input prompt indicates a desired property of a replacement portion; and 
 generating, by the foundation model, the replacement portion, wherein the replacement portion replaces the selected portion of the chemical structure and includes a generated chemical structure predicted to have the desired property. 
   
     
     
         15 . The system of  claim 14 , further comprising:
 receiving, at the foundation model, user-input interactions and user-input constraints for modifying the replacement portion; and   generating, by the foundation model, a modified chemical structure for the replacement portion based on one or more of the user-input interactions, and the user-input constraints.   
     
     
         16 . The system of  claim 14 , further comprising:
 receiving user feedback based on the generated chemical structure; and   tuning the foundation model based on one or more of the user input selection, the user-input prompt, the generated chemical structure, and the user feedback.   
     
     
         17 . The system of  claim 14 , wherein the user-input prompt comprises at least one of natural language prompts, boundary conditions, and a combination of natural language and explicit property settings. 
     
     
         18 . The system of  claim 14 , wherein the user-input prompt comprises user-input property settings that indicate the desired property of the replacement portion, wherein the user-input property settings comprise one or more of a physical property, a chemical property, a thermal property, and a mechanical property. 
     
     
         19 . The system of  claim 14 , further comprising;
 receiving, at the foundation model, one or more of user-input interaction data, user selection data, or user-input constraint data to generate a prompt based on in-context learning of the one or more of user-input interaction data, user selection data, and user-input constraint data.   
     
     
         20 . A computer program product for materials discovery and design, the computer program product comprising a computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation comprising:
 providing, using one or more processors, a chemical structure at a design interface;   receiving, at a foundation model, a user selection of a portion of the chemical structure and a user-input prompt for replacing the portion, wherein the user-input prompt indicates a desired property of a replacement portion; and   generating, by the foundation model, the replacement portion, wherein the replacement portion replaces the selected portion of the chemical structure and includes a generated chemical structure predicted to have the desired property.   
     
     
         21 . The computer program product of  claim 20 , further comprising:
 receiving, at the foundation model, user-input interactions and user-input constraints for modifying the replacement portion; and   generating, by the foundation model, a modified chemical structure for the replacement portion based on one or more of the user-input interactions, and user-input constraints.   
     
     
         22 . The computer program product of  claim 20 , further comprising:
 receiving user feedback based on the generated chemical structure; and   tuning the foundation model based on one or more of the user input selection, the user-input prompt, the generated chemical structure, and the user feedback.   
     
     
         23 . The computer program product of  claim 20 , wherein the user-input prompt comprises at least one of natural language prompts, boundary conditions, and a combination of natural language and explicit property settings. 
     
     
         24 . The computer program product of  claim 20 , wherein the user-input prompt comprises user-input property settings that indicates the desired property of the replacement portion, wherein the user-input property settings comprise one or more of a physical property, a chemical property, a thermal property, and a mechanical property.